mlsanityData to 14 Oct 2026

A Low-Cost, High-Speed, and Robust Bin Picking System for Factory Automation Enabled by a Non-Stop, Multi-View, and Active Vision Scheme

arXiv 2410.00706 ↗First posted 1 Oct 2024cs.RO cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Bin picking systems in factory automation usually face robustness issues caused by sparse and noisy 3D data of metallic objects. Utilizing multiple views, especially with a one-shot 3D sensor and "sensor on hand" configuration is getting more popularity due to its effectiveness, flexibility, and low cost. While moving the 3D sensor to acquire multiple views for 3D fusion, joint optimization, or active vision suffers from low-speed issues. That is because sensing is taken as a decoupled module from motion tasks and is not intentionally designed for a bin picking system. To address the problems, we designed a bin picking system, which tightly couples a multi-view, active vision scheme with motion tasks in a "sensor on hand" configuration. It not only speeds up the system by parallelizing the high-speed sensing scheme to the robot place action but also decides the next sensing path to maintain the continuity of the whole picking process. Unlike others focusing only on sensing evaluation, we also evaluated our design by picking experiments on 5 different types of objects without human intervention. Our experiments show the whole sensing scheme can be finished within 1.682 seconds (maximum) on CPU and the average picking complete rate is over 97.75%. Due to the parallelization with robot motion, the sensing scheme accounts for only 0.635 seconds in takt time on average.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

15 of 23 references.

References · 15

  1. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 495
  2. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  3. 2017Intel RealSense Stereoscopic Depth Camerascited by 83
  4. 2015Recovering 6D Object Pose and Predicting Next-Best-View in the Crowdcited by 79
  5. 2016Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challengecited by 70
  6. 2023BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objectscited by 70
  7. 2020MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusioncited by 46
  8. 2021Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Imagecited by 42
  9. 2019Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Pickingcited by 25
  10. 2021ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Pickingcited by 22
  11. 2022MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Networkcited by 9
  12. 2018Real-Time 6D Object Pose Estimation on CPUcited by 7
3 more
  1. 20226D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimizationcited by 7
  2. 2021Probabilistic Multi-View Fusion of Active Stereo Depth Maps for Robotic Bin-Pickingcited by 4
  3. 2022Next-Best-View Prediction for Active Stereo Cameras and Highly Reflective Objectscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Framescited by 3
  2. 2026Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Pickingcited by 0
  3. 2026RYOPO: Bringing End-to-End Category-Level Object Pose Estimation into Real Timecited by 0